DOCSLIB.ORG

KOF Working Papers, No

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
KOF Working Papers, No A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Maag, Thomas Working Paper On the accuracy of the probability method for quantifying beliefs about inflation KOF Working Papers, No. 230 Provided in Cooperation with: KOF Swiss Economic Institute, ETH Zurich Suggested Citation: Maag, Thomas (2009) : On the accuracy of the probability method for quantifying beliefs about inflation, KOF Working Papers, No. 230, ETH Zurich, KOF Swiss Economic Institute, Zurich, http://dx.doi.org/10.3929/ethz-a-005859391 This Version is available at: http://hdl.handle.net/10419/50407 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen Nutzungsbedingungen die in der dort Content Licence (especially Creative Commons Licences), you genannten Lizenz gewährten Nutzungsrechte. may exercise further usage rights as specified in the indicated licence. www.econstor.eu KOF Working Papers On the Accuracy of the Probability Method for Quantifying Beliefs about Inflation Thomas Maag No. 230 July 2009 ETH Zurich KOF Swiss Economic Institute WEH D 4 Weinbergstrasse 35 8092 Zurich Switzerland Phone +41 44 632 42 39 Fax +41 44 632 12 18 www.kof.ethz.ch [email protected] On the Accuracy of the Probability Method for Quantifying Beliefs About Inflation∗ Thomas Maagy KOF Working Paper No. 230, July 2009 This Version: April 2010 Abstract This paper assesses the probability method for quantifying EU consumer survey data on perceived and expected inflation. Based on household level data from the Swedish consumer survey that asks for both qualitative and quantitative responses, it is found that the theoretical assumptions of the method do not hold. In particular, estimates of unrestricted response schemes indicate that response intervals are asymmetric and that qualitative inflation expectations are formed relative to perceptions of current inflation. Nevertheless, the probability method generates series that are highly correlated with the mean of actual quantitative beliefs. For quantifying the cross-sectional dispersion of beliefs, however, an index of qualitative variation is most accurate. JEL classification: C53, D84, E31. Keywords: quantification, inflation expectations, inflation perceptions, qualitative response data, belief formation. ∗I thank Simone Elmer, Sarah M. Lein, Christoph Moser, Rolf Schenker and seminar participants at the KOF Swiss Economic Institute for helpful comments and suggestions. I am grateful to the Swedish National Institute of Economic Research for providing access to the Consumer Tendency Survey data. Financial support by the Swiss National Science Foundation is gratefully acknowledged. yKOF Swiss Economic Institute, ETH Zurich, CH-8092 Zurich, Switzerland, E-mail: [email protected] 1 1 Introduction Surveys of households and firms are often qualitative. Rather than giving a quantitative estimate of a particular variable, respondents are asked to indicate their beliefs on qualitative scales. In the European Union (EU), beliefs of households about inflation are surveyed as part of the Joint Harmonized EU Consumer Survey programme. Within this framework, har- monized qualitative surveys are conducted in all member states, covering a national sample size of roughly 1,500 households on a monthly basis. The EU consumer survey thus provides an extensive and consistent dataset on beliefs about inflation.1 In particular, the EU con- sumer survey both asks for perceptions about current inflation and expectations about future inflation. Consequently, the response data has been investigated by a large literature. Only recently, the euro cash changeover and its effects on inflation perceptions of households has given rise to a new strand of research.2 Since the EU consumer survey is qualitative, most empirical applications rely on a method to quantify the qualitative response data in the first place. This paper assesses the validity of one particular method, the probability method for 5-category scales, and compares its accuracy to other quantification approaches. Possibly the most widely used quantification method is the balance statistic proposed by Anderson (1952). It is originally defined as the difference between the share of respondents that perceive or expect positive inflation rates and the share of respondents that perceive or expect negative inflation rates. Theil (1952) rationalizes the balance statistic, demonstrating that it is an appropriate measure of the population mean if quantitative beliefs are uniformly distributed. Furthermore, Theil (1952) suggests that the distributional assumption may be relaxed by imposing a normal distribution instead. Combined with the assumption that re- spondents perceive or expect prices to be constant in qualitative terms if their quantitative 1Currently, the Joint Harmonized EU Consumer Survey covers a monthly sample of roughly 40,000 con- sumers in 27 member states. The consumer survey consists of 15 qualitative questions pertaining to the household's financial situation, perceived economic conditions and planned savings and spending. The ques- tionnaire is translated into national languages and may include additional country specific questions, see European Commission (2007). 2This literature centers on the rise in perceived inflation coinciding with the euro cash changeover, as doc- umented in ECB (2005). Several explanations are being discussed, including increased information processing requirements due to conversion rates, overreaction to prices of frequently bought items and anchoring of per- ceptions to prior expectations. See, e.g., Ehrmann (2006), Aucremanne, Collin, and Stragier (2007), Doehring and Mordonu (2007), Dziuda and Mastrobuoni (2006), Aalto-Set¨al¨a(2006) and Fluch and Stix (2007). Ab- stracting from the euro cash changeover, other contributions use the EU consumer survey data to investigate belief formation in general, see, e.g., D¨opke, Dovern, Fritsche, and Slacalek (2008), Forsells and Kenny (2004), Lamla and Lein (2008) and Lein and Maag (2008). 2 belief is within an indifference interval around 0%, the mean and variance of the imposed distribution can be identified. The model of Theil (1952) has been rediscovered by Carl- son and Parkin (1975) and is known today as the Carlson-Parkin method or the 3-category probability method.3 Batchelor and Orr (1988) extend the probability method to response data on 5-category scales as it is available from the EU consumer survey. Taking into ac- count the particular wording of the EU consumer survey, Berk (1999) additionally suggests an identification scheme that links inflation expectations to inflation perceptions. The goal of this paper is to assesses the 5-category probability method and to derive lessons for applied research. The analysis relies on joining qualitative and quantitative response data on household-level, taken from the Swedish Consumer Tendency Survey. To the best of my knowledge, only two studies have investigated surveys that ask for both qualitative and quantitative responses. Defris and Williams (1979) consider a 5-year sample from an Australian consumer survey. They document that the balance statistic as well as the 3- category probability method generate series that are only weakly correlated with quantitative survey responses. Batchelor (1986) investigates micro-data from the University of Michigan Survey of Consumers. In line with Defris and Williams (1979), Batchelor (1986) finds that both the balance statistic and quantified expectations generated with the probability method are inaccurate, in particular in the short term. This result is in contrast to my findings for Sweden. This paper extends the literature in several respects. First, it provides a detailed as- sessment of the theoretical assumptions underlying the 5-category probability method. Ex- isting research focuses on the 3-category probability method and on testing distributional assumptions. Joining quantitative and qualitative responses on household-level allows to es- timate unrestricted response schemes. The restrictions imposed by the 5-category probability method can then be tested using likelihood theory. Second, the accuracy of the 5-category probability method relative to the mean and cross-sectional standard deviation of quantitative responses is assessed in a long sample of 154 monthly surveys spanning 01/1996{10/2008. The discussion centers on comparing correlation coefficients relying on the Fisher z-transformation and double block bootstrap confidence intervals. Accuracy is compared to a set of alternative 3A less common quantification method is the regression approach of Pesaran (1987). The regression method extends the balance statistic, allowing for a non-linear relation between response
Load more

Recommended publications
  • Ocr IB 1967 '70
    ocr IB 1967 '70 OAK RIDGE NATIONAL LABORATORY operated by UNION CARBIDE CORPORATION NUCLEAR DIVISION for the • U.S. ATOMIC ENERGY COMMISSION ORNL - TM- 1919 INDICES OF QUALITATIVE VARIATION Allen R. Wilcox NOTICE This document contains information of a preliminary natur<> and was prepared primarily far internal use at the Oak Ridge National Laboratory. It is subject to revision or correction and therefore doe> not represent o finol report. DISCLAIMER This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency Thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. DISCLAIMER Portions of this document may be illegible in electronic image products. Images are produced from the best available original document. .-------------------LEGAL NOTICE--------------~ This report was prepared as on account of Government sponsored work. Neither the United States, nor the Commission, nor any person acting on behalf of the Commission: A. Makes any warranty or representation, expressed or implied, with respect to the accuracy, compl""t"'n~<~~:oo;, nr ulltf!fulnes!l: of the information conto1ned in this report, or that the use of any information, apparatus, method, or process disc lased in this report may not infringe privately ownsd rights; or B.
    [Show full text]
  • Current Practices in Quantitative Literacy © 2006 by the Mathematical Association of America (Incorporated)
    Current Practices in Quantitative Literacy © 2006 by The Mathematical Association of America (Incorporated) Library of Congress Catalog Card Number 2005937262 Print edition ISBN: 978-0-88385-180-7 Electronic edition ISBN: 978-0-88385-978-0 Printed in the United States of America Current Printing (last digit): 10 9 8 7 6 5 4 3 2 1 Current Practices in Quantitative Literacy edited by Rick Gillman Valparaiso University Published and Distributed by The Mathematical Association of America The MAA Notes Series, started in 1982, addresses a broad range of topics and themes of interest to all who are in- volved with undergraduate mathematics. The volumes in this series are readable, informative, and useful, and help the mathematical community keep up with developments of importance to mathematics. Council on Publications Roger Nelsen, Chair Notes Editorial Board Sr. Barbara E. Reynolds, Editor Stephen B Maurer, Editor-Elect Paul E. Fishback, Associate Editor Jack Bookman Annalisa Crannell Rosalie Dance William E. Fenton Michael K. May Mark Parker Susan F. Pustejovsky Sharon C. Ross David J. Sprows Andrius Tamulis MAA Notes 14. Mathematical Writing, by Donald E. Knuth, Tracy Larrabee, and Paul M. Roberts. 16. Using Writing to Teach Mathematics, Andrew Sterrett, Editor. 17. Priming the Calculus Pump: Innovations and Resources, Committee on Calculus Reform and the First Two Years, a subcomit- tee of the Committee on the Undergraduate Program in Mathematics, Thomas W. Tucker, Editor. 18. Models for Undergraduate Research in Mathematics, Lester Senechal, Editor. 19. Visualization in Teaching and Learning Mathematics, Committee on Computers in Mathematics Education, Steve Cunningham and Walter S.
    [Show full text]
  • Measuring Variation for Nominal Data
    Bulletin ofthe Psychonomic Society 1988. 26 (5), 433-436 Measuring variation for nominal data TARALD O. KVALSETH University of Minnesota , Minneapolis, Minnesota This paper is concerned with the measurement of variation or dispersion for nominal categori­ cal data. A new measure of such variation is proposed. It is defined as the complement of the standard deviation of the individual category frequencies from the modal frequency. Large-sample distribution methods are developed for constructing confidence intervals and testing hypotheses for the equivalent population measure of variation. Numerical sample data are used to exem­ plify the statistical inference methods. For measuring the variation or dispersion in a set of measures, the IQV in Equation 2 appears to be the most nominal data, a number ofalternative measures have been widely accepted, especially in the social sciences (e.g., defined in the literature. Such measures are generally Ott, Mendenhall, & Larson, 1978, pp. 110-113). The functions ofthe absolute frequencies or counts nh n2, . .. , measure in Equation 3, which is based on Shannon's n, for a set of I categories that are exhaustive and mutu­ (1948) entropy in information theory, has been used quite ally exclusive. Thus, among the total number ofn = Elnl extensively in the biological sciences (Pielou, 1977). observations (individuals or items), n, observations be­ The measure MOM in Equation 1 suffers from one seri­ long to category i, with each observation belonging to one ous limitation: its value is determined exclusively by the and only one category. Among the best known variation relative modal frequency nm/n and I (the number of measures for the n, (i = 1, 2, .
    [Show full text]
  • Media Diversity and the Analysis of Qualitative Variation
    Media diversity and the analysis of qualitative variation David Deacon & James Stanyer CENTRE FOR RESEARCH IN COMMUNICATION AND CULTURE, LOUGHBOROUGH UNIVERSITY January 2019 1 Abstract Diversity is recognised as a significant criterion for appraising the democratic performance of media systems. This article begins by considering key conceptual debates that help differentiate types and levels of diversity. It then addresses one of the core methodological challenges in measuring diversity: how do we model statistical variation and difference when many measures of source and content diversity only attain the nominal level of measurement? We identify a range of obscure statistical indices developed in other fields that measure the strength of ‘qualitative variation’. Using original data, we compare the performance of five diversity indices and, on this basis, propose the creation of a more effective diversity average measure (DIVa). The article concludes by outlining innovative strategies for drawing statistical inferences from these measures, using bootstrapping and permutation testing resampling. All statistical procedures are supported by a unique online resource developed with this article. Key word: Media diversity, diversity indices, qualitative variation, categorical data Acknowledgements We wish to thank Orange Gao and Peter Dean of Business Intelligence and Strategy Ltd and Dr Adrian Leguina, School of Social Sciences, Loughborough University for their assistance in developing the conceptual and technical aspects of this paper. 2 Introduction Concerns about diversity are at the heart of discussions about the democratic performance of all media platforms (e.g. Entman, 1989: 64, 95; Habermas, 2006: 412, 416; Sunstein, 2018:85). With mainstream media, such debates are fundamentally about plurality, namely, the extent to which these powerful opinion leading organisations are able and/or inclined to engage with and represent the disparate communities, issues and interests that sculpt modern societies.
    [Show full text]
  • The Need to Measure Variance in Experiential Learning and a New Statistic to Do So
    Developments in Business Simulation & Experiential Learning, Volume 26, 1999 THE NEED TO MEASURE VARIANCE IN EXPERIENTIAL LEARNING AND A NEW STATISTIC TO DO SO John R. Dickinson, University of Windsor James W. Gentry, University of Nebraska-Lincoln ABSTRACT concern should be given to the learning process rather than performance outcomes. The measurement of variation of qualitative or categorical variables serves a variety of essential We contend that insight into process can be gained purposes in simulation gaming: de- via examination of within-student and across-student scribing/summarizing participants’ decisions for variance in decision-making. For example, if the evaluation and feedback, describing/summarizing role of an experiential exercise is to foster trial-and- simulation results toward the same ends, and error learning, the variance in a student’s decisions explaining variation for basic research purposes. will be greatest when the trial-and-error stage is at Extant measures of dispersion are not well known. its peak. To the extent that the exercise works as Too, simulation gaming situations are peculiar in intended, greater variances would be expected early that the total number of observations available-- in the game play as opposed to later, barring the usually the number of decision periods or the game world encountering a discontinuity. number of participants--is often small. Each extant measure of dispersion has its own limitations. On the other hand, it is possible that decisions may Common limitations are that, under ordinary be more consistent early and then more variable later circumstances and especially with small numbers if the student is trying to recover from early of observations, a measure may not be defined and missteps.
    [Show full text]
  • TRACE: Tennessee Research and Creative Exchange
    University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 12-2016 On the quantification of complexity and diversity from phenotypes to ecosystems Zachary Harrison Marion University of Tennessee, Knoxville, [email protected] Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Biostatistics Commons, and the Other Ecology and Evolutionary Biology Commons Recommended Citation Marion, Zachary Harrison, "On the quantification of complexity and diversity from phenotypes to ecosystems. " PhD diss., University of Tennessee, 2016. https://trace.tennessee.edu/utk_graddiss/4104 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Zachary Harrison Marion entitled "On the quantification of complexity and diversity from phenotypes to ecosystems." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the equirr ements for the degree of Doctor of Philosophy, with a major in Ecology and Evolutionary Biology. Benjamin M. Fitzpatrick, Major Professor We have read this dissertation and recommend its acceptance: James A. Fordyce, Nathan J. Sanders, Shawn R. Campagna Accepted for the Council: Carolyn R. Hodges Vice Provost and Dean of the Graduate School (Original signatures are on file with official studentecor r ds.) On the quantification of complexity and diversity from phenotypes to ecosystems A Dissertation Presented for the Doctor of Philosophy Degree The University of Tennessee, Knoxville Zachary Harrison Marion December 2016 c by Zachary Harrison Marion, 2016 All Rights Reserved.
    [Show full text]
  • Measures of Dispersion and Serial Dependence in Categorical Time Series
    econometrics Article Measures of Dispersion and Serial Dependence in Categorical Time Series Christian H. Weiß Department of Mathematics and Statistics, Helmut Schmidt University, 22043 Hamburg, Germany; [email protected] Received: 21 December 2018; Accepted: 17 April 2019; Published: 22 April 2019 Abstract: The analysis and modeling of categorical time series requires quantifying the extent of dispersion and serial dependence. The dispersion of categorical data is commonly measured by Gini index or entropy, but also the recently proposed extropy measure can be used for this purpose. Regarding signed serial dependence in categorical time series, we consider three types of k-measures. By analyzing bias properties, it is shown that always one of the k-measures is related to one of the above-mentioned dispersion measures. For doing statistical inference based on the sample versions of these dispersion and dependence measures, knowledge on their distribution is required. Therefore, we study the asymptotic distributions and bias corrections of the considered dispersion and dependence measures, and we investigate the finite-sample performance of the resulting asymptotic approximations with simulations. The application of the measures is illustrated with real-data examples from politics, economics and biology. Keywords: Cohen’s k; extropy; nominal variation; signed serial dependence; asymptotic distribution 1. Introduction In many applications, the available data are not of quantitative nature (e.g., real numbers or counts) but consist of observations from a given finite set of categories. In the present article, we are concerned with data about political goals in Germany, fear states in the stock market, and phrases in a bird’s song.
    [Show full text]
  • Elementary Statistics for Social Planners
    Elementary Statistics for Social Planners Hayden Brown Social Policy Unit 2013 1 | P a g e 2 | P a g e CONTENTS Tables ....................................................................................................... 5 Types of social data .................................................................................. 5 Characteristics of Social Measures ............................................................................... 5 Contents of these notes................................................................................................ 7 Measures of Central Tendency ................................................................. 7 Mode ........................................................................................................................... 7 Median ......................................................................................................................... 7 Arithmetic Mean .......................................................................................................... 7 Measures of Dispersion ............................................................................ 7 Nominal Data ............................................................................................................... 7 Variation ratio ......................................................................................................................7 Index of Qualitative Variation ..............................................................................................7 Index of Diversity
    [Show full text]
  • Phonological Variation and the Construction of Regional Identities in New Zealand English
    Phonological variation and the construction of regional identities in New Zealand English by Sharon Marsden A thesis submitted to Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy Victoria University of Wellington 2013 i Abstract This thesis addresses the ongoing evolution of New Zealand English phonology. In particular it explores the links between phonological variation and the social identities of speakers. The thesis investigates the possible emergence of regional dialects in the ongoing development of the variety. The investigation contributes to theories of dialect development, especially in relation to linguistic varieties described as “postcolonial” English. Since the onset of linguistic research on New Zealand English, scholars have highlighted the remarkable geographical uniformity of the variety. However, recent research concerning the development of postcolonial Englishes suggests that regional diversity is inevitable, but that its occurrence is tied to the construction of sociocultural identities. In this thesis I apply a holistic approach to investigating phonological variation and the construction of regional identities in modern New Zealand English. My aim in this thesis is twofold: firstly, to investigate current trends in 21st century New Zealand English phonology and secondly, to gain insights into the linguistic, social and cultural processes associated with the birth of new regional dialects. I view the ongoing evolution of Englishes as involving a composite of wide-ranging factors from the linguistic, historical, social, cultural and ideological domains. In order to address the full complexities of the issues I track variation and change in one influential and important dimension of English phonological systems: rhoticity.
    [Show full text]
  • Social Statistics
    SOCIAL STATISTICS Németh, Renáta Simon, Dávid Created by XMLmind XSL-FO Converter. SOCIAL STATISTICS írta Németh, Renáta és Simon, Dávid Publication date 2011 Created by XMLmind XSL-FO Converter. Tartalom Introduction ........................................................................................................................................ v 1. Lecture 1 ......................................................................................................................................... 1 1. What is social statistics? Why do we need it? ....................................................................... 1 2. Interpretation pitfall I: Ecological fallacy ............................................................................. 1 3. Interpretation pitfall II: An empirical relationship does not imply causation ........................ 2 4. Interpretation pitfall III: Simpson‟s paradox ......................................................................... 2 2. Lecture 2 ......................................................................................................................................... 6 1. Role of statistics in social research ....................................................................................... 6 2. Levels of measurement ......................................................................................................... 8 3. Unit of analysis ..................................................................................................................... 9 3. Lecture
    [Show full text]
  • Understanding the Index of Qualitative Variation
    Chapter 5: The Importance of Measuring Why is Variability important? Variability Simple Example: Suppose you wanted to • Measures of Central Tendency - know how satisfied CNAs are with their Numbers that describe what is typical or job and you found that the average average (central) in a distribution (e.g., mean, mode, median). score (on a five point scal)le) was “3”. • Measures of Variability - Numbers that describe diversity or variability in the How would knowing the variability help distribution (e.g., range, index of qualitative you to understand how satisfied CNAs variation, interquartile range, box plot, are with their job? variance, standard deviation). Answer: It would help you to see whether the average score of “3” means that the majority of CNAs are neutral about their jobs or that there is a split with CNAs either feeling very good (score of 5) or very bad (score of 1) about their job (average of 1’s and 5’s = 3). Another example. A measure of variability: Understanding the Index of Index of Qualitative Variation Qualitative Variation (IQV) • The IQV is a single number that expresses – A measure of variability for nominal variables. the diversity of a distribution. - A measure that shows how diverse a particular category •The IQV ranges from 0 to 1 of a variable is. • An IQV of 0 would indicate that the distribution has NO diversity at all. For example, when looking at ethnic diversity among •An IQV of 1 would indicate that the states, some states are very diverse with multiple ethnic distribution is maximally diverse. groups and lots of people in each group.
    [Show full text]
  • Stochastic Algorithms for Biochemical Processes
    UNIVERSITA` DEGLI STUDI DI MILANO BICOCCA Facolt`adi Scienze MM.FF.NN. Dottorato di Ricerca in Informatica XXII Ciclo Stochastic algorithms for biochemical processes Paolo Cazzaniga Supervisor Prof. Giancarlo Mauri Tutor Prof. Paola Bonizzoni PhD Coordinator Prof. Stefania Bandini ANNO ACCADEMICO 2008–2009 iii Acknowledgments I would like to express my sincere gratitude to Professor Giancarlo Mauri who has been my supervisor. He gave me many helpful suggestions and important advice during the course of my PhD. Special thanks are due to Daniela Besozzi for her constant encourage- ment, her useful help (on many occasions) and her friendship. I would also like to thank Dario Pescini for his assistance with writing code, his great effort to explain things and for all the last minute rushes to airports and train stations because we were always late. A particular thanks to Prof. Stephen Gilmore and Jane Hillston who hosted me for 6 months in Edinburgh. And I should also say thank you to my flatmates, the guys working at the LFCS and all the other people I met there. I wish to thank the people working at DISCo for their friendship, help and for the parties we had. I am grateful to the people who helped me during my PhD, and to my friends for the fun and laughter we shared. Indeed, I owe a special thanks to my family for its support during this period and to Laura who believed in me and supported me throughout my PhD. Contents Introduction ix 1 Stochastic algorithms for the simulation of biochemical sys- tems 1 1.1 Gillespie’s stochastic simulation algorithm .
    [Show full text]